EP4201315A1 - Method and system for screening cardiac conditions of a patient - Google Patents
Method and system for screening cardiac conditions of a patient Download PDFInfo
- Publication number
- EP4201315A1 EP4201315A1 EP22152851.6A EP22152851A EP4201315A1 EP 4201315 A1 EP4201315 A1 EP 4201315A1 EP 22152851 A EP22152851 A EP 22152851A EP 4201315 A1 EP4201315 A1 EP 4201315A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- data
- patient
- therapy
- survey
- level
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 77
- 230000000747 cardiac effect Effects 0.000 title claims abstract description 69
- 238000012216 screening Methods 0.000 title claims abstract description 34
- 238000002560 therapeutic procedure Methods 0.000 claims abstract description 48
- 238000007670 refining Methods 0.000 claims abstract description 7
- 230000029058 respiratory gaseous exchange Effects 0.000 claims description 17
- 238000009423 ventilation Methods 0.000 claims description 17
- 238000004590 computer program Methods 0.000 claims description 11
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 10
- 229910052760 oxygen Inorganic materials 0.000 claims description 10
- 239000001301 oxygen Substances 0.000 claims description 10
- 230000000241 respiratory effect Effects 0.000 claims description 4
- 230000036772 blood pressure Effects 0.000 claims description 3
- 230000037396 body weight Effects 0.000 claims description 2
- 201000002859 sleep apnea Diseases 0.000 description 28
- 230000008901 benefit Effects 0.000 description 10
- 206010019280 Heart failures Diseases 0.000 description 9
- 238000013459 approach Methods 0.000 description 6
- 238000012502 risk assessment Methods 0.000 description 6
- 208000024891 symptom Diseases 0.000 description 6
- 238000001514 detection method Methods 0.000 description 4
- 238000007477 logistic regression Methods 0.000 description 4
- 230000002093 peripheral effect Effects 0.000 description 4
- 206010030113 Oedema Diseases 0.000 description 3
- 230000001419 dependent effect Effects 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 208000000059 Dyspnea Diseases 0.000 description 2
- 206010013975 Dyspnoeas Diseases 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- DDRJAANPRJIHGJ-UHFFFAOYSA-N creatinine Chemical compound CN1CC(=O)NC1=N DDRJAANPRJIHGJ-UHFFFAOYSA-N 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 206010016256 fatigue Diseases 0.000 description 2
- 230000007958 sleep Effects 0.000 description 2
- 208000019116 sleep disease Diseases 0.000 description 2
- 208000020685 sleep-wake disease Diseases 0.000 description 2
- 102000009027 Albumins Human genes 0.000 description 1
- 108010088751 Albumins Proteins 0.000 description 1
- 208000020446 Cardiac disease Diseases 0.000 description 1
- 206010007559 Cardiac failure congestive Diseases 0.000 description 1
- 208000024172 Cardiovascular disease Diseases 0.000 description 1
- 206010008501 Cheyne-Stokes respiration Diseases 0.000 description 1
- 208000007590 Disorders of Excessive Somnolence Diseases 0.000 description 1
- 206010020772 Hypertension Diseases 0.000 description 1
- 206010021079 Hypopnoea Diseases 0.000 description 1
- 206010021143 Hypoxia Diseases 0.000 description 1
- DGAQECJNVWCQMB-PUAWFVPOSA-M Ilexoside XXIX Chemical compound C[C@@H]1CC[C@@]2(CC[C@@]3(C(=CC[C@H]4[C@]3(CC[C@@H]5[C@@]4(CC[C@@H](C5(C)C)OS(=O)(=O)[O-])C)C)[C@@H]2[C@]1(C)O)C)C(=O)O[C@H]6[C@@H]([C@H]([C@@H]([C@H](O6)CO)O)O)O.[Na+] DGAQECJNVWCQMB-PUAWFVPOSA-M 0.000 description 1
- 206010049235 Nocturnal dyspnoea Diseases 0.000 description 1
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 1
- 206010047295 Ventricular hypertrophy Diseases 0.000 description 1
- 208000008784 apnea Diseases 0.000 description 1
- 230000004872 arterial blood pressure Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000009530 blood pressure measurement Methods 0.000 description 1
- 238000009534 blood test Methods 0.000 description 1
- 229940109239 creatinine Drugs 0.000 description 1
- 238000013399 early diagnosis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000005548 health behavior Effects 0.000 description 1
- 208000019622 heart disease Diseases 0.000 description 1
- 230000001146 hypoxic effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000002107 myocardial effect Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 229910052700 potassium Inorganic materials 0.000 description 1
- 239000011591 potassium Substances 0.000 description 1
- 230000002685 pulmonary effect Effects 0.000 description 1
- 238000011867 re-evaluation Methods 0.000 description 1
- 208000013220 shortness of breath Diseases 0.000 description 1
- 229910052708 sodium Inorganic materials 0.000 description 1
- 239000011734 sodium Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000008961 swelling Effects 0.000 description 1
- 230000002195 synergetic effect Effects 0.000 description 1
- 230000004584 weight gain Effects 0.000 description 1
- 235000019786 weight gain Nutrition 0.000 description 1
- 230000003936 working memory Effects 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B7/00—Instruments for auscultation
- A61B7/003—Detecting lung or respiration noise
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M16/00—Devices for influencing the respiratory system of patients by gas treatment, e.g. mouth-to-mouth respiration; Tracheal tubes
- A61M16/0003—Accessories therefor, e.g. sensors, vibrators, negative pressure
Definitions
- the present invention relates to patient screening and, in particular, the present invention relates to a computer-implemented method for screening cardiac conditions of a patient, a medical system, and a computer program element.
- Heart failure, HF, or other cardiac conditions are highly prevalent in Sleep Disorder Breathing, SDB, population and are often undiagnosed.
- SDB Sleep Disorder Breathing
- the number of hospitalizations and the mortality rate increase in cardiac failure patients with associated SDB.
- Most patients with HF and SDB often complain about similar symptoms which makes it challenging to diagnose both.
- SDB Sleep disorder breathing
- SDB cardiovascular diseases
- HF or other cardiac conditions are highly prevalent in SDB population; however, they are often undiagnosed.
- the number of hospitalizations and the mortality rate increase in cardiac failure patients with associated SDB.
- SDB causes myocardial as well as arterial damage. Untreated SDB might therefore promote the progression of cardiac disease, resulting in heart failure and increased mortality in patients with heart failure.
- SDB is strongly related and causally contributes to hypertension, HTN, the most common risk factor for ventricular hypertrophy and HF.
- a method for screening cardiac conditions of a patient comprises.
- the present invention advantageously allows an early cardiac condition detection in terms of a benefit for the patient for early and unobtrusive detection of underlying cardiac conditions in SDB patients and consequently early management of the condition.
- the patient could eliminate the burden of the condition on their quality of life.
- the present invention advantageously allows that the patient therefore can learn early on how to manage health behaviors and risk factors and improve their lifestyle. Cardiac conditions remain the main cause of morbidity and mortality and consequently, early diagnosis is of utmost importance.
- the present invention advantageously allows lowering the burden of the healthcare system, early detection of cardiac conditions also means lowering the burden to the healthcare system.
- Health care expenditures related to cardiac conditions are overwhelming. For example, congestive heart failure affects many people and consumes much value in health-care expenditures annually.
- the present invention advantageously allows lowering the burden of screening all SDB patients in terms of lowering the burden of screening all SDB patients for cardiac conditions by following a 2-fold assessment.
- This approach benefits the healthcare provider by allowing to better spend their time and prioritize the patients that need their help without having to spend their time to screen all SDB patients, including those of the lowest risk of developing or having a cardiac condition.
- the present invention advantageously allows a tiered screening approach to detect HF or other cardiac conditions within SDB population and lower the burden of screening all SDB patients for cardiac conditions by use of PAP data for initial risk assessment.
- the present invention advantageously introduces an incremental approach where an initial risk assessment is performed based on therapy data. For the identified high-risk patient, a second assessment is performed taking into account additional device and patient reported data, aiming to increase the certainty of the risk assessment.
- Detecting heart failure or cardiac patients among SDB patients using PAP or alternative therapy data in combination with other available sensors can help to lower the burden of screening all SDB patients for cardiac conditions and also to early detect cardiac conditions and manage them on an early stage.
- the method may be computer-implemented.
- a medical system may be operated and/or a medical procedure carried out by utilizing the medical system may be performed in an autonomous operating mode.
- This may be a semi-autonomous operating mode, in which one or more, but not all, procedure steps are supported by human intervention, e.g. by medical staff or technicians, and one or more, but not all, procedure steps are performed in an automatic manner by the medical system, or which may be a fully-autonomous operating mode, in which at least the actual medical procedure.
- the fully-autonomous operating mode may also concern one or more steps of the procedure in general.
- the therapy sensor data comprises sensor data on ventilation pressure, ventilation volume, ventilation airflow rate, or oxygen partial pressure provided during respiratory ventilation as the therapy of the patient.
- the therapy sensor data comprises sensor data on a breath rate of the patient or respiration characteristics of the patient, the sensor data provided by at least one microphone recording the sound of breathing of the patient.
- the survey data comprises data of a push survey provided by a survey of the patient.
- the survey of the patent is conducted using an application program on a mobile electronic equipment.
- the device data comprises data blood pressure and body weight.
- the device data is provided using an application program on a mobile electronic equipment.
- the method further comprises the steps of further refining the refined cardiac risk value by additional data of the first data source or the second data source or a third data source and by means of a n-th level of a multi-level procedure; wherein n is greater than 2.
- a medical screening system for screening cardiac conditions of a patient, the medical screening system comprises a first data source, a second data source and at least one processor.
- the at least one processor is configured to calculate a cardiac risk value based on therapy sensor data using a first level of a two-level procedure, wherein the therapy sensor data is provided from the first data source as sensor data during a therapy of the patient.
- the at least one processor is configured to refine the calculated cardiac risk value based on survey data and/or device data using a second level of a two-level procedure to provide a refined cardiac risk value, wherein the survey data and/or the device data is provided from the second data source by incremental data gathering.
- the first data source is a sensor configured to measure sensor data on ventilation pressure, ventilation volume, ventilation airflow rate, or oxygen partial pressure provided during respiratory ventilation as the therapy of the patient.
- the ventilation airflow rate may advantageously be defined as a more granular measurement when compared to others, for instance volume as amount of airflow over time.
- the first data source comprises a microphone configured to record the sound of breathing of the patient and the first data source is configured to determine a breath rate of the patient or respiration characteristics of the patient based on the recorded sound of the breathing of the patient.
- the second data source is implemented on a mobile electronic equipment and the second data source is configured to provide the survey data comprising data of a push survey provided by a survey of the patient, wherein the survey of the patent is conducted using an application program on a mobile electronic equipment.
- a computer program element which when executed by a processor is configured to carry out the method of the first aspect, and/or to control a system according to the second aspect.
- a computer-readable storage or transmission medium which has stored or which carries the computer program element according to the third aspect.
- Fig. 1 shows in a schematic block diagram a medical screening system 100 according to an exemplary embodiment of the present invention.
- the medical screening system 100 comprises a first data source 110, a second data source 120 and at least one processor 130.
- the at least one processor 130 is configured to calculate a cardiac risk value based on therapy sensor data using a first level of a two-level procedure, wherein the therapy sensor data is provided from the first data source as sensor data during a therapy of the patient.
- the at least one processor 130 is configured to refine the calculated cardiac risk value based on survey data and/or device data using a second level of a two-level procedure to provide a refined cardiac risk value, wherein the survey data and/or the device data is provided from the second data source by incremental data gathering.
- the first data source 110 is configured to provide the therapy sensor data as sensor data during a therapy of the patient and during a first level of a two level procedure.
- the second data source 120 is configured to provide the survey data and/or the device data by incremental data gathering and during a second level of the two-level procedure.
- Fig. 2 shows in a flow chart of a method for screening cardiac conditions of a patient according to an exemplary embodiment of the present invention.
- calculating S1 a cardiac risk value based on therapy sensor data by means of a first level of a multi-level procedure is performed, wherein the therapy sensor data is provided from a first data source as sensor data during a therapy of the patient.
- a second step refining S2 the calculated cardiac risk value based on survey data and/or device data by means of a second level of a multi-level procedure to provide a refined cardiac risk value is conducted, wherein the survey data and/or the device data is provided from a second data source by incremental data gathering.
- the method for screening cardiac conditions of a patient comprises a 2-level scoring algorithm in terms of a 2- or multi-level procedure to calculate a score that indicates a risk of the patient having HF or other cardiac conditions.
- a risk is estimated based on therapy data or other sensor data already available to the patient.
- the assessment is performed by taking into account additional data collected from the patient (either device or self-reported data).
- the algorithm can be implemented as part of the solution already available to the patient, therapy device or mobile application, mobile and internet application.
- the present invention provides the advantages of identify patients at risk in an unobtrusive way.
- the present invention provides the advantage of allowing for later re-evaluation if risk is not assessed as high.
- the present invention provides the advantage of an increased certainty of the assessment by collecting additional data.
- the present invention provides the advantage of incremental approach to allow users activate or deactivate elements, or data points, of their interest or elements that might or might not be available to them.
- the present invention provides the advantages of low cost in its execution.
- the present invention allows in advantageous manner for integration of any additional available device, for example an activity tracker, or any smart phone.
- the present invention allows in advantageous manner avoiding any burden to the patient as it requires very limited self-reported information.
- the present invention provides the advantage of an increase in specificity (and hence precision) of follow-up care provided to the patient.
- the present invention provides the advantage of a contribution to the overall improvement in early detection of a clinical condition of the patient.
- the method for screening cardiac conditions of a patient comprises an incremental approach: For the first level of the 2-level scoring algorithm in terms of a 2-level procedure or multi-level procedure, there may be provided an assessment based on PAP data or alternative therapies, e.g. positional therapy.
- the first level of a multi-level procedure may comprise a signal or an alert signal for possible cardiac conditions based on PAP data and partial oxygen pressure or peripheral oxygen saturation, SpO2.
- the data tracking as provided by any data source i.e. the first or the second data source may comprise change respiration, breath rate, (respiration characteristics of patient), (sound of breathing from home audio devices), etc.
- the data tracking include peripheral oxygen saturation, SpO2 data.
- the second level of a multi-level procedure may comprise incremental data gathering. For part of population with a flag of high risk of cardiac condition
- the second level of a multi-level procedure may comprise a push survey, for instance, a New York Heart Association NYHA approved data survey or patient reported data, to therapy users with symptoms through PAP device of mobile app, such as the Philips DreamMapper application.
- a push survey for instance, a New York Heart Association NYHA approved data survey or patient reported data
- PAP device of mobile app such as the Philips DreamMapper application.
- the second level of a multi-level procedure may comprise Additional input from connected devices tracking weight and blood pressure.
- the following modules that are used to calculate a final score estimating a risk of the patient having a cardiac condition.
- a first and a second data sources are used as data input modules.
- the first data source may be used.
- the data for the first phase assessment can contain data from the therapy device (PAP or positional therapy), peripheral oxygen saturation, SpO2 sensor data, data from wearable device and smartphone data.
- the second level of a multi-level procedure may comprise that additional device data is required and additional patient reported survey data.
- the first assessment data input may comprise therapy data: PAP or positional therapy data about flow and pressure, RR, heart rate, inter-beat intervals, IBIs, apneas, hypopneas, and Cheyne Stokes respiration.
- the first assessment data input may comprise peripheral oxygen saturation, SpO2, sensor data: providing an oxygen saturation and hypoxic burden measurements
- the first assessment data input may comprise wearable device data: any wearable device already available to the patient can be used as additional source of data to increase the risk prediction accuracy.
- Example of typically available data via such devices is accelerometer data or heart rate
- the first assessment data input may comprise smartphone data: e.g. heart rate HR, sound of breathing, etc.
- the first level of a multi-level procedure may comprise using an algorithmic module for the first phase risk assessment implementing a logistic regression model predicting risk of heart failure, HF, or cardiac condition based on the data inputs mentioned before.
- the first level of a multi-level procedure may comprise using an output of this model is a risk indication: high, medium and low. Based on this indication a follow up step is defined according to one of the exemplary embodiments as follows: According to an exemplary embodiment of the invention, if patient risk is high then the second assessment phase is performed to increase the risk estimation accuracy.
- the assessment is repeated in a follow up time defined by the healthcare provider, for example this may extend only a few weeks, months, for example two weeks.
- the risk is estimated as low then no direct actions are required.
- the user can decide if assessment should be repeated in a follow up time, or if patient's conditions change.
- the model output may comprise: Outcome of the logistic regression model P as a probability of having a cardiac condition. Thresholds defined by the user can be applied to identify the patient with the highest risk.
- the intercept ⁇ and coefficients ⁇ n can be adjusted to avoid over- and underestimation. For that adjustment, we need to fit the logistic model on real data which becomes available after a pilot test of the algorithm for an SDB cohort.
- the second assessment data input in terms of the survey data and/or device data by means of a second level of a multi-level procedure comprises additional device data: e.g. weight measurement, blood pressure measurement.
- the second assessment data input in terms of the survey data and/or device data by means of a second level of a multi-level procedure comprises self-reported data: survey data is collected via PAP machine or a mobile application.
- the survey data is collected via PAP machine or a mobile application.
- the second assessment data input in terms of the survey data and/or device data by means of a second level of a multi-level procedure comprises data on push some home test suggestion for patients to evaluate themselves to further improve the accuracy of the second assessment.
- the second assessment data input in terms of the survey data and/or device data by means of a second level of a multi-level procedure comprises data on sudden weight gain, a walk test, suggest to perform annual physical exam and obtain blood tests (e.g. sodium, potassium, albumin, creatinine, LDL, etc.)
- the algorithmic module for the second phase risk assessment based on the survey data and/or device data by means of a second level of a multi-level procedure comprises a second risk model based on logistic regression is used to estimate the risk of HF or other cardiac condition.
- the Input features for this model are all data points collected in both first and second phase assessment.
- Output of the logistic regression model P is a probability of having a cardiac condition. This probability would indicate a risk of having a cardiac condition and will be used by the clinical provider to direct the OSA patient to a cardiologist specialist for further investigation of the possible cardiac conditions.
- the output of the model will be classified to high, medium and low risk similarly to the first model described by using thresholds defined by the user. Based on this output an alert could be raised if the score is sufficiently high on any given day. Alternatively, an averaged score over the last days will be used. Again, the user is free to choose on how many of the latest days they would like to calculate a risk average.
- Fig. 3 shows a flow chart of a two-level algorithm or a two-level procedure for screening cardiac conditions of a patient according to an embodiment.
- the therapy data TD, SpO2 data SPD, wearable data WD or smartphone data SD may be used as input data or therapy sensor data for the first level of a multi-level procedure, i.e. the therapy sensor data is provided from a first data source as sensor data during a therapy of the patient.
- the risk of HF or cardiac condition may be determined, if the determined value is below a predefined threshold.
- a subgroup of patients with medium cardiac or HF risk may be determined.
- a waiting time in terms of days, weeks or months for a newly initiated re-assessing of the risk may be awaited.
- the second level when performing step F21, may start with continuing for the subgroup of patients with high cardiac risk or HF risk as determined in F11.
- the assessment certainty by refining may be achieved using additional device data add, or additional survey data ASD.
- the refined calculated risk when performing step F23, the refined calculated risk may be provided.
- the computer program element might be able to provide all necessary steps to fulfill the procedure of an exemplary embodiment of the method as described above.
- a computer readable medium such as a CD-ROM, USB stick or the like
- the computer readable medium has a computer program element stored on it which computer program element is described by the preceding section.
- a computer program may be stored and/or distributed on a suitable medium, such as an optical storage medium or a solid state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the internet or other wired or wireless telecommunication systems.
- a suitable medium such as an optical storage medium or a solid state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the internet or other wired or wireless telecommunication systems.
- the computer program may also be presented over a network like the World Wide Web and can be downloaded into the working memory of a data processor from such a network.
- a medium for making a computer program element available for downloading is provided, which computer program element is arranged to perform a method according to one of the previously described embodiments of the invention.
Abstract
The present invention relates to a method for screening cardiac conditions of a patient, the method comprising the following steps: calculating (S1) a cardiac risk value based on therapy sensor data by means of a first level of a multi-level procedure, wherein the therapy sensor data is provided from a first data source as sensor data during a therapy of the patient; and
refining (S2) the calculated cardiac risk value based on survey data and/or device data by means of a second level of a multi-level procedure to provide a refined cardiac risk value, wherein the survey data and/or the device data is provided from a second data source by incremental data gathering.
refining (S2) the calculated cardiac risk value based on survey data and/or device data by means of a second level of a multi-level procedure to provide a refined cardiac risk value, wherein the survey data and/or the device data is provided from a second data source by incremental data gathering.
Description
- The present invention relates to patient screening and, in particular, the present invention relates to a computer-implemented method for screening cardiac conditions of a patient, a medical system, and a computer program element.
- Heart failure, HF, or other cardiac conditions are highly prevalent in Sleep Disorder Breathing, SDB, population and are often undiagnosed. The number of hospitalizations and the mortality rate increase in cardiac failure patients with associated SDB. Most patients with HF and SDB often complain about similar symptoms which makes it challenging to diagnose both.
- Sleep disorder breathing, SDB, is a condition characterized by interrupted breathing during sleep, for longer than 10 seconds at least 5 times per hour, on average, throughout the sleep period. A lot of patients with SDB have multiple comorbidities. These comorbidities have a significant impact on healthcare use and mortality in patients with SDB. The comorbidity burden progressively increases with SDB severity.
- In particular, there is a strong association between SDB and cardiovascular diseases. HF or other cardiac conditions are highly prevalent in SDB population; however, they are often undiagnosed. The number of hospitalizations and the mortality rate increase in cardiac failure patients with associated SDB. SDB causes myocardial as well as arterial damage. Untreated SDB might therefore promote the progression of cardiac disease, resulting in heart failure and increased mortality in patients with heart failure. SDB is strongly related and causally contributes to hypertension, HTN, the most common risk factor for ventricular hypertrophy and HF.
- Most patients with HF and SDB often complain about similar symptoms which makes it challenging to diagnose both. Symptoms often common in both conditions are fatigue, daytime somnolence, shortness of breath, edema, and nocturnal dyspnea. Due to these overlapping symptoms cardiac conditions might get overlooked in diagnosed SDB population.
- Screening all SDB patients for cardiac conditions would be extremely time consuming and would put a huge burden to healthcare providers and healthcare system.
- There may, therefore, be a need for improved means for a tiered screening approach to detect HF or cardiac patients within SDB population and to lower the burden of screening all SDB patients for cardiac conditions by use of pulmonary arterial pressure, PAP, data for initial risk assessment. The object of the present invention is solved by the subject matter of the independent claims, wherein further embodiments are incorporated in the dependent claims.
- According to a first aspect, there is provided a method for screening cardiac conditions of a patient. The method comprises.
- calculating a cardiac risk value based on therapy sensor data by means of a first level of a multi-level procedure, wherein the therapy sensor data is provided from a first data source as sensor data during a therapy of the patient; and
- refining the calculated cardiac risk value based on survey data and/or device data by means of a second level of a multi-level procedure to provide a refined cardiac risk value, wherein the survey data and/or the device data is provided from a second data source by incremental data gathering.
- In this way, a method of detecting heart failure or other cardiac conditions is provided in SDB patients using PAP or alternative therapy data in combination with other available sensors.
- The present invention advantageously allows an early cardiac condition detection in terms of a benefit for the patient for early and unobtrusive detection of underlying cardiac conditions in SDB patients and consequently early management of the condition.
- By managing their condition at an early stage, the patient could eliminate the burden of the condition on their quality of life. The present invention advantageously allows that the patient therefore can learn early on how to manage health behaviors and risk factors and improve their lifestyle. Cardiac conditions remain the main cause of morbidity and mortality and consequently, early diagnosis is of utmost importance.
- The present invention advantageously allows lowering the burden of the healthcare system, early detection of cardiac conditions also means lowering the burden to the healthcare system. Health care expenditures related to cardiac conditions are overwhelming. For example, congestive heart failure affects many people and consumes much value in health-care expenditures annually.
- The present invention advantageously allows lowering the burden of screening all SDB patients in terms of lowering the burden of screening all SDB patients for cardiac conditions by following a 2-fold assessment. This approach benefits the healthcare provider by allowing to better spend their time and prioritize the patients that need their help without having to spend their time to screen all SDB patients, including those of the lowest risk of developing or having a cardiac condition.
- The present invention advantageously allows a tiered screening approach to detect HF or other cardiac conditions within SDB population and lower the burden of screening all SDB patients for cardiac conditions by use of PAP data for initial risk assessment. The present invention advantageously introduces an incremental approach where an initial risk assessment is performed based on therapy data. For the identified high-risk patient, a second assessment is performed taking into account additional device and patient reported data, aiming to increase the certainty of the risk assessment.
- Detecting heart failure or cardiac patients among SDB patients using PAP or alternative therapy data in combination with other available sensors can help to lower the burden of screening all SDB patients for cardiac conditions and also to early detect cardiac conditions and manage them on an early stage.
- Preferably, the method may be computer-implemented. Further preferably, a medical system may be operated and/or a medical procedure carried out by utilizing the medical system may be performed in an autonomous operating mode. This may be a semi-autonomous operating mode, in which one or more, but not all, procedure steps are supported by human intervention, e.g. by medical staff or technicians, and one or more, but not all, procedure steps are performed in an automatic manner by the medical system, or which may be a fully-autonomous operating mode, in which at least the actual medical procedure. Optionally, the fully-autonomous operating mode may also concern one or more steps of the procedure in general.
- According to an exemplary embodiment of the present invention, the therapy sensor data comprises sensor data on ventilation pressure, ventilation volume, ventilation airflow rate, or oxygen partial pressure provided during respiratory ventilation as the therapy of the patient.
- According to an exemplary embodiment of the present invention, the therapy sensor data comprises sensor data on a breath rate of the patient or respiration characteristics of the patient, the sensor data provided by at least one microphone recording the sound of breathing of the patient.
- According to an exemplary embodiment of the present invention, the survey data comprises data of a push survey provided by a survey of the patient.
- According to an exemplary embodiment of the present invention, the survey of the patent is conducted using an application program on a mobile electronic equipment.
- According to an exemplary embodiment of the present invention, the device data comprises data blood pressure and body weight.
- According to an exemplary embodiment of the present invention, the device data is provided using an application program on a mobile electronic equipment.
- According to an exemplary embodiment of the present invention, the method further comprises the steps of further refining the refined cardiac risk value by additional data of the first data source or the second data source or a third data source and by means of a n-th level of a multi-level procedure; wherein n is greater than 2.
- According to a second aspect, there is provided a medical screening system for screening cardiac conditions of a patient, the medical screening system comprises a first data source, a second data source and at least one processor.
- The at least one processor is configured to calculate a cardiac risk value based on therapy sensor data using a first level of a two-level procedure, wherein the therapy sensor data is provided from the first data source as sensor data during a therapy of the patient.
- The at least one processor is configured to refine the calculated cardiac risk value based on survey data and/or device data using a second level of a two-level procedure to provide a refined cardiac risk value, wherein the survey data and/or the device data is provided from the second data source by incremental data gathering.
- According to an exemplary embodiment of the present invention, the first data source is a sensor configured to measure sensor data on ventilation pressure, ventilation volume, ventilation airflow rate, or oxygen partial pressure provided during respiratory ventilation as the therapy of the patient. According to an exemplary embodiment of the present invention, the ventilation airflow rate may advantageously be defined as a more granular measurement when compared to others, for instance volume as amount of airflow over time.
- According to an exemplary embodiment of the present invention, the first data source comprises a microphone configured to record the sound of breathing of the patient and the first data source is configured to determine a breath rate of the patient or respiration characteristics of the patient based on the recorded sound of the breathing of the patient.
- According to an exemplary embodiment of the present invention, the second data source is implemented on a mobile electronic equipment and the second data source is configured to provide the survey data comprising data of a push survey provided by a survey of the patient, wherein the survey of the patent is conducted using an application program on a mobile electronic equipment.
- According to a third aspect, there is provided a computer program element, which when executed by a processor is configured to carry out the method of the first aspect, and/or to control a system according to the second aspect.
- According to a fourth aspect, there is provided a computer-readable storage or transmission medium, which has stored or which carries the computer program element according to the third aspect.
- It is noted that the above embodiments may be combined with each other irrespective of the aspect involved. Accordingly, the method may be combined with structural features of the system of the other aspects and, likewise, the system may be combined with features described above with regard to the method.
- These and other aspects of the present invention will become apparent from and elucidated with reference to the embodiments described hereinafter.
- Exemplary embodiments of the invention will be described in the following drawings.
-
Fig 1 shows in a schematic block diagram a medical screening system for screening cardiac conditions of a patient according to an exemplary embodiment of the present invention. -
Fig. 2 shows a flow chart of a method for screening cardiac conditions of a patient according to an exemplary embodiment of the present invention. -
Fig. 3 shows a flow chart of a two-level algorithm or a two-level procedure for screening cardiac conditions of a patient according to an exemplary embodiment of the present invention. -
Fig. 1 shows in a schematic block diagram amedical screening system 100 according to an exemplary embodiment of the present invention. - The
medical screening system 100 comprises afirst data source 110, asecond data source 120 and at least oneprocessor 130. - The at least one
processor 130 is configured to calculate a cardiac risk value based on therapy sensor data using a first level of a two-level procedure, wherein the therapy sensor data is provided from the first data source as sensor data during a therapy of the patient. - The at least one
processor 130 is configured to refine the calculated cardiac risk value based on survey data and/or device data using a second level of a two-level procedure to provide a refined cardiac risk value, wherein the survey data and/or the device data is provided from the second data source by incremental data gathering. - The
first data source 110 is configured to provide the therapy sensor data as sensor data during a therapy of the patient and during a first level of a two level procedure. - The
second data source 120 is configured to provide the survey data and/or the device data by incremental data gathering and during a second level of the two-level procedure. -
Fig. 2 shows in a flow chart of a method for screening cardiac conditions of a patient according to an exemplary embodiment of the present invention. - In a first step, calculating S1 a cardiac risk value based on therapy sensor data by means of a first level of a multi-level procedure is performed, wherein the therapy sensor data is provided from a first data source as sensor data during a therapy of the patient.
- In a second step, refining S2 the calculated cardiac risk value based on survey data and/or device data by means of a second level of a multi-level procedure to provide a refined cardiac risk value is conducted, wherein the survey data and/or the device data is provided from a second data source by incremental data gathering.
- According to an exemplary embodiment of the invention, the method for screening cardiac conditions of a patient comprises a 2-level scoring algorithm in terms of a 2- or multi-level procedure to calculate a score that indicates a risk of the patient having HF or other cardiac conditions.
- According to an exemplary embodiment of the invention, throughout the first assessment phase, a risk is estimated based on therapy data or other sensor data already available to the patient. At the second phase, the assessment is performed by taking into account additional data collected from the patient (either device or self-reported data).
- According to an exemplary embodiment of the invention, the algorithm can be implemented as part of the solution already available to the patient, therapy device or mobile application, mobile and internet application.
- The present invention provides the advantages of identify patients at risk in an unobtrusive way.
- According to an exemplary embodiment of the present invention, the present invention provides the advantage of allowing for later re-evaluation if risk is not assessed as high.
- According to an exemplary embodiment of the present invention, the present invention provides the advantage of an increased certainty of the assessment by collecting additional data.
- According to an exemplary embodiment of the present invention, the present invention provides the advantage of incremental approach to allow users activate or deactivate elements, or data points, of their interest or elements that might or might not be available to them.
- According to an exemplary embodiment of the present invention, the present invention provides the advantages of low cost in its execution.
- According to an exemplary embodiment of the present invention, the present invention allows in advantageous manner for integration of any additional available device, for example an activity tracker, or any smart phone.
- According to an exemplary embodiment of the present invention, the present invention allows in advantageous manner avoiding any burden to the patient as it requires very limited self-reported information.
- According to an exemplary embodiment of the present invention, the present invention provides the advantage of an increase in specificity (and hence precision) of follow-up care provided to the patient.
- According to an exemplary embodiment of the present invention, the present invention provides the advantage of a contribution to the overall improvement in early detection of a clinical condition of the patient.
- According to an exemplary embodiment of the invention, the method for screening cardiac conditions of a patient comprises an incremental approach:
For the first level of the 2-level scoring algorithm in terms of a 2-level procedure or multi-level procedure, there may be provided an assessment based on PAP data or alternative therapies, e.g. positional therapy. - According to an exemplary embodiment of the invention, the first level of a multi-level procedure may comprise a signal or an alert signal for possible cardiac conditions based on PAP data and partial oxygen pressure or peripheral oxygen saturation, SpO2.
- According to an exemplary embodiment of the invention, the data tracking as provided by any data source, i.e. the first or the second data source may comprise change respiration, breath rate, (respiration characteristics of patient), (sound of breathing from home audio devices), etc.
- According to an exemplary embodiment of the invention, the data tracking include peripheral oxygen saturation, SpO2 data.
- According to an exemplary embodiment of the invention, the second level of a multi-level procedure may comprise incremental data gathering. For part of population with a flag of high risk of cardiac condition
- According to an exemplary embodiment of the invention, the second level of a multi-level procedure may comprise a push survey, for instance, a New York Heart Association NYHA approved data survey or patient reported data, to therapy users with symptoms through PAP device of mobile app, such as the Philips DreamMapper application.
- According to an exemplary embodiment of the invention, the second level of a multi-level procedure may comprise Additional input from connected devices tracking weight and blood pressure.
- According to an exemplary embodiment of the invention, the following modules that are used to calculate a final score estimating a risk of the patient having a cardiac condition.
- According to an exemplary embodiment of the invention, as data input modules a first and a second data sources are used. For the first level, the first data source may be used. The data for the first phase assessment can contain data from the therapy device (PAP or positional therapy), peripheral oxygen saturation, SpO2 sensor data, data from wearable device and smartphone data.
- According to an exemplary embodiment of the invention, the second level of a multi-level procedure may comprise that additional device data is required and additional patient reported survey data.
- According to an exemplary embodiment of the invention, the first assessment data input may comprise therapy data: PAP or positional therapy data about flow and pressure, RR, heart rate, inter-beat intervals, IBIs, apneas, hypopneas, and Cheyne Stokes respiration.
- According to an exemplary embodiment of the invention, the first assessment data input may comprise peripheral oxygen saturation, SpO2, sensor data: providing an oxygen saturation and hypoxic burden measurements
- According to an exemplary embodiment of the invention, the first assessment data input may comprise wearable device data: any wearable device already available to the patient can be used as additional source of data to increase the risk prediction accuracy. Example of typically available data via such devices is accelerometer data or heart rate
- According to an exemplary embodiment of the invention, the first assessment data input may comprise smartphone data: e.g. heart rate HR, sound of breathing, etc.
- According to an exemplary embodiment of the invention, the first level of a multi-level procedure may comprise using an algorithmic module for the first phase risk assessment implementing a logistic regression model predicting risk of heart failure, HF, or cardiac condition based on the data inputs mentioned before.
- According to an exemplary embodiment of the invention, the first level of a multi-level procedure may comprise using an output of this model is a risk indication: high, medium and low. Based on this indication a follow up step is defined according to one of the exemplary embodiments as follows:
According to an exemplary embodiment of the invention, if patient risk is high then the second assessment phase is performed to increase the risk estimation accuracy. - According to an exemplary embodiment of the invention, if the risk is medium then the assessment is repeated in a follow up time defined by the healthcare provider, for example this may extend only a few weeks, months, for example two weeks.
- According to an exemplary embodiment of the invention, if the risk is estimated as low then no direct actions are required. The user can decide if assessment should be repeated in a follow up time, or if patient's conditions change.
- According to an exemplary embodiment of the invention, the model output may comprise: Outcome of the logistic regression model P as a probability of having a cardiac condition. Thresholds defined by the user can be applied to identify the patient with the highest risk.
-
- According to an exemplary embodiment of the invention, the intercept β and coefficients αn can be adjusted to avoid over- and underestimation. For that adjustment, we need to fit the logistic model on real data which becomes available after a pilot test of the algorithm for an SDB cohort.
- According to an exemplary embodiment of the invention, the second assessment data input in terms of the survey data and/or device data by means of a second level of a multi-level procedure comprises additional device data: e.g. weight measurement, blood pressure measurement.
- According to an exemplary embodiment of the invention, the second assessment data input in terms of the survey data and/or device data by means of a second level of a multi-level procedure comprises self-reported data: survey data is collected via PAP machine or a mobile application.
- According to an exemplary embodiment of the invention, when performing method using the Philips DreamMapper application where the patient is asked to report certain symptoms for example data on breathlessness, fatigue, oedema (known as fluid retention, dropsy, hydropsy and swelling, is the build-up of fluid in the body's tissue), and answer a question needed to estimate the NYHA class, for example an indication of HF severity, the survey data is collected via PAP machine or a mobile application.
- According to an exemplary embodiment of the invention, the second assessment data input in terms of the survey data and/or device data by means of a second level of a multi-level procedure comprises data on push some home test suggestion for patients to evaluate themselves to further improve the accuracy of the second assessment.
- According to an exemplary embodiment of the invention, the second assessment data input in terms of the survey data and/or device data by means of a second level of a multi-level procedure comprises data on sudden weight gain, a walk test, suggest to perform annual physical exam and obtain blood tests (e.g. sodium, potassium, albumin, creatinine, LDL, etc.)
- According to an exemplary embodiment of the invention, the algorithmic module for the second phase risk assessment based on the survey data and/or device data by means of a second level of a multi-level procedure comprises a second risk model based on logistic regression is used to estimate the risk of HF or other cardiac condition.
- According to an exemplary embodiment of the invention, the Input features for this model are all data points collected in both first and second phase assessment. Output of the logistic regression model P is a probability of having a cardiac condition. This probability would indicate a risk of having a cardiac condition and will be used by the clinical provider to direct the OSA patient to a cardiologist specialist for further investigation of the possible cardiac conditions.
- According to an exemplary embodiment of the invention, the output of the model will be classified to high, medium and low risk similarly to the first model described by using thresholds defined by the user. Based on this output an alert could be raised if the score is sufficiently high on any given day. Alternatively, an averaged score over the last days will be used. Again, the user is free to choose on how many of the latest days they would like to calculate a risk average.
-
Fig. 3 shows a flow chart of a two-level algorithm or a two-level procedure for screening cardiac conditions of a patient according to an embodiment. - According to an exemplary embodiment of the invention, the therapy data TD, SpO2 data SPD, wearable data WD or smartphone data SD may be used as input data or therapy sensor data for the first level of a multi-level procedure, i.e. the therapy sensor data is provided from a first data source as sensor data during a therapy of the patient.
- According to an exemplary embodiment of the invention, during the first level of a multi-level procedure, preferably a two-level procedure, in step F11 the risk of HF or cardiac condition may be determined, if the determined value is below a predefined threshold.
- According to an exemplary embodiment of the invention, when performing step F12, a subgroup of patients with medium cardiac or HF risk may be determined.
According to an exemplary embodiment of the invention, by executing step F13, a waiting time in terms of days, weeks or months for a newly initiated re-assessing of the risk may be awaited. - According to an exemplary embodiment of the invention, when performing step F21, the second level may start with continuing for the subgroup of patients with high cardiac risk or HF risk as determined in F11.
- According to an exemplary embodiment of the invention, when performing step F22 the assessment certainty by refining may be achieved using additional device data add, or additional survey data ASD.
- According to an exemplary embodiment of the invention, when performing step F23, the refined calculated risk may be provided.
- Further, the computer program element might be able to provide all necessary steps to fulfill the procedure of an exemplary embodiment of the method as described above.
- According to a further exemplary embodiment of the present invention, a computer readable medium, such as a CD-ROM, USB stick or the like, is presented wherein the computer readable medium has a computer program element stored on it which computer program element is described by the preceding section.
- A computer program may be stored and/or distributed on a suitable medium, such as an optical storage medium or a solid state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the internet or other wired or wireless telecommunication systems.
- However, the computer program may also be presented over a network like the World Wide Web and can be downloaded into the working memory of a data processor from such a network. According to a further exemplary embodiment of the present invention, a medium for making a computer program element available for downloading is provided, which computer program element is arranged to perform a method according to one of the previously described embodiments of the invention.
- It is noted that embodiments of the invention are described with reference to different subject matters. In particular, some embodiments are described with reference to method type claims whereas other embodiments are described with reference to the device type claims.
- However, a person skilled in the art will gather from the above and the following description that, unless otherwise notified, in addition to any combination of features belonging to one type of subject matter also any combination between features relating to different subject matters is considered to be disclosed with this application. However, all features can be combined providing synergetic effects that are more than the simple summation of the features.
- While the invention has been illustrated and described in detail in the drawings and the foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive. The invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing a claimed invention, from a study of the drawings, the disclosure, and the dependent claims.
- In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items re-cited in the claims. The mere fact that certain measures are re-cited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.
Claims (15)
- A method for screening cardiac conditions of a patient, the method comprising the following steps:calculating (S1) a cardiac risk value based on therapy sensor data by means of a first level of a multi-level procedure, wherein the therapy sensor data is provided from a first data source as sensor data during a therapy of the patient; andrefining (S2) the calculated cardiac risk value based on survey data and/or device data by means of a second level of a multi-level procedure to provide a refined cardiac risk value, wherein the survey data and/or the device data is provided from a second data source by incremental data gathering.
- The method of claim 1, wherein the therapy sensor data comprises sensor data on ventilation pressure, ventilation volume, ventilation airflow rate, or oxygen partial pressure provided during respiratory ventilation as the therapy of the patient.
- The method of claim 1 or 2, wherein the therapy sensor data comprises sensor data on a breath rate of the patient or respiration characteristics of the patient, the sensor data provided by at least one microphone recording the sound of breathing of the patient.
- The method of any one of the preceding claims, wherein the survey data comprises data of a push survey of the patient.
- The method of claim 4, wherein the push survey of the patent is conducted using an application program on a mobile electronic equipment.
- The method of any one of the preceding claims, wherein the device data comprises data on blood pressure and/or on body weight.
- The method of claim 6, wherein the device data is provided using an application program on a mobile electronic equipment.
- The method of any one of the preceding claims, wherein the method further comprises the steps of further refining the refined cardiac risk value by additional data of the first data source or the second data source or a third data source and by means of a n-th level of a multi-level procedure; wherein n is greater than 2.
- A medical screening system (100) for screening cardiac conditions of a patient, the medical screening system (100) comprising:a first data source (110);a second data source (120);at least one processor (130), at least configured to calculate a cardiac risk value based on therapy sensor data using a first level of a two-level procedure, wherein the therapy sensor data is provided from the first data source (110) as sensor data during a therapy of the patient; wherein the at least one processor (130) is further configured to refine the calculated cardiac risk value based on survey data and/or device data using a second level of a two-level procedure to provide a refined cardiac risk value, wherein the survey data and/or the device data is provided from the second data source (120) by incremental data gathering.
- The medical screening system of claim 9, wherein the first data source (110) is a sensor configured to measure sensor data on ventilation pressure, ventilation volume, ventilation airflow rate, or oxygen partial pressure provided during respiratory ventilation as the therapy of the patient.
- The medical screening system of claim 9 or 10, wherein the first data source (110) comprises a microphone configured to record the sound of breathing of the patient and the first data source (110) is configured to determine a breath rate of the patient or respiration characteristics of the patient based on the recorded sound of the breathing of the patient.
- The medical screening system of claim any one of the preceding claims 9 to 11, wherein the second data source (120) is implemented on a mobile electronic equipment and the second data source (120) is configured to provide the survey data comprising data of a push survey provided by a survey of the patient, wherein the survey of the patent is conducted using an application program on a mobile electronic equipment.
- A computer program element, which when executed by a processor is configured to carry out the method of any one of claims 1 to 8, and/or to control a medical screening system of claims 9 to 12.
- A data carrier signal comprising therapy sensor data used by the method of any one of claims 1 to 8.
- A data carrier signal comprising survey data and/or device data used by the method of any one of claims 1 to 8.
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US18/080,862 US20230200745A1 (en) | 2021-12-23 | 2022-12-14 | Method and system for screening cardiac conditions of a patient |
PCT/EP2022/086216 WO2023117709A1 (en) | 2021-12-23 | 2022-12-15 | Method and system for screening cardiac conditions of a patient |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US202163293203P | 2021-12-23 | 2021-12-23 |
Publications (1)
Publication Number | Publication Date |
---|---|
EP4201315A1 true EP4201315A1 (en) | 2023-06-28 |
Family
ID=80001451
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP22152851.6A Pending EP4201315A1 (en) | 2021-12-23 | 2022-01-24 | Method and system for screening cardiac conditions of a patient |
Country Status (2)
Country | Link |
---|---|
US (1) | US20230200745A1 (en) |
EP (1) | EP4201315A1 (en) |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20030149453A1 (en) * | 2002-02-07 | 2003-08-07 | Kroll Mark W. | System and method for evaluating risk of mortality due to congestive heart failure using physiologic sensors |
US20080157980A1 (en) * | 2006-12-27 | 2008-07-03 | Cardiac Pacemakers, Inc. | Within-patient algorithm to predict heart failure decompensation |
US20150250428A1 (en) * | 2014-03-07 | 2015-09-10 | Cardiac Pacemakers, Inc. | Heart failure event detection using multi-level categorical fusion |
US20150342487A1 (en) * | 2014-06-02 | 2015-12-03 | Cardiac Pacemakers, Inc. | Systems and methods for evaluating hemodynamic response to atrial fibrillation |
US20210353166A1 (en) * | 2018-09-07 | 2021-11-18 | Transformative AI Ltd | Analysis of cardiac data |
-
2022
- 2022-01-24 EP EP22152851.6A patent/EP4201315A1/en active Pending
- 2022-12-14 US US18/080,862 patent/US20230200745A1/en active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20030149453A1 (en) * | 2002-02-07 | 2003-08-07 | Kroll Mark W. | System and method for evaluating risk of mortality due to congestive heart failure using physiologic sensors |
US20080157980A1 (en) * | 2006-12-27 | 2008-07-03 | Cardiac Pacemakers, Inc. | Within-patient algorithm to predict heart failure decompensation |
US20150250428A1 (en) * | 2014-03-07 | 2015-09-10 | Cardiac Pacemakers, Inc. | Heart failure event detection using multi-level categorical fusion |
US20150342487A1 (en) * | 2014-06-02 | 2015-12-03 | Cardiac Pacemakers, Inc. | Systems and methods for evaluating hemodynamic response to atrial fibrillation |
US20210353166A1 (en) * | 2018-09-07 | 2021-11-18 | Transformative AI Ltd | Analysis of cardiac data |
Also Published As
Publication number | Publication date |
---|---|
US20230200745A1 (en) | 2023-06-29 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
JP6049620B2 (en) | Medical scoring system and method | |
JP4981925B2 (en) | Inter-patient comparison for risk stratification | |
US9629548B2 (en) | Within-patient algorithm to predict heart failure decompensation | |
US10311533B2 (en) | Method and system to enable physician labels on a remote server and use labels to verify and improve algorithm results | |
US9022930B2 (en) | Inter-relation between within-patient decompensation detection algorithm and between-patient stratifier to manage HF patients in a more efficient manner | |
JP6692355B2 (en) | A method for score confidence interval estimation when vital sign sampling frequency is limited | |
JP2018503885A5 (en) | System for predicting medical precursor events and method for operating the system | |
JP2018533798A (en) | Prediction of acute respiratory syndrome (ARDS) based on patient's physiological response | |
JP2018534697A (en) | System and method for facilitating health monitoring based on personalized predictive models | |
JP6534192B2 (en) | Patient health composite score distribution and / or representative composite score based thereon | |
CN115721284A (en) | Health monitoring system, method, electronic device and storage medium | |
EP3251590A1 (en) | Method and system for monitoring blood pressure in real-time, and non-transitory computer-readable recording medium | |
EP4201315A1 (en) | Method and system for screening cardiac conditions of a patient | |
WO2023117709A1 (en) | Method and system for screening cardiac conditions of a patient | |
EP3420898A1 (en) | Assessing delirium in a subject | |
Khan et al. | Severe analysis of cardiac disease detection using the wearable device by artificial intelligence | |
Zhu et al. | An intelligent cardiac health monitoring and review system | |
Santos et al. | Performance of early warning scoring systems to detect patient deterioration in the emergency department | |
EP4123662A1 (en) | Monitoring subjects after discharge | |
US20160106330A1 (en) | Cardiac monitoring device and method | |
Santos | Vital-sign data-fusion methods to identify patient deterioration in the emergency department | |
US20160171170A1 (en) | Measuring respiration or other periodic physiological processes | |
Wenerski et al. | Simulation of medical data as a way of speeding up development of algorithms for a system for estimation of patient's health | |
WO2017055949A1 (en) | Clinical decision support for differential diagnosis of pulmonary edema in critically ill patients | |
Blokhuis | Wireless monitoring of high-risk patients using a wearable patch sensor: a clinical validation study |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PUAI | Public reference made under article 153(3) epc to a published international application that has entered the european phase |
Free format text: ORIGINAL CODE: 0009012 |
|
STAA | Information on the status of an ep patent application or granted ep patent |
Free format text: STATUS: THE APPLICATION HAS BEEN PUBLISHED |
|
AK | Designated contracting states |
Kind code of ref document: A1 Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR |
|
STAA | Information on the status of an ep patent application or granted ep patent |
Free format text: STATUS: THE APPLICATION IS DEEMED TO BE WITHDRAWN |